The first geostationary ocean color satellite sensor, Geostationary Ocean Color Imager (GOCI), which is onboard South Korean Communication, Ocean, and Meteorological Satellite (COMS), was successfully launched in June of 2010. GOCI has a local area coverage of the western Pacific region centered at around 36°N and 130°E and covers ~2500 × 2500 km2. GOCI has eight spectral bands from 412 to 865 nm with an hourly measurement during daytime from 9:00 to 16:00 local time, i.e., eight images per day. In a collaboration between NOAA Center for Satellite Applications and Research (STAR) and Korea Institute of Ocean Science and Technology (KIOST), we have been working on deriving and improving GOCI ocean color products, e.g., normalized water-leaving radiance spectra (nLw(λ)), chlorophyll-a concentration, diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), etc. The GOCI-covered ocean region includes one of the world’s most turbid and optically complex waters. To improve the GOCI-derived nLw(λ) spectra, a new atmospheric correction algorithm was developed and implemented in the GOCI ocean color data processing. The new algorithm was developed specifically for GOCI-like ocean color data processing for this highly turbid western Pacific region. In this paper, we show GOCI ocean color results from our collaboration effort. From in situ validation analyses, ocean color products derived from the new GOCI ocean color data processing have been significantly improved. Generally, the new GOCI ocean color products have a comparable data quality as those from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua. We show that GOCI-derived ocean color data can provide an effective tool to monitor ocean phenomenon in the region such as tide-induced re-suspension of sediments, diurnal variation of ocean optical and biogeochemical properties, and horizontal advection of river discharge. In particular, we show some examples of ocean diurnal variations in the region, which can be provided effectively from satellite geostationary measurements.
© 2013 OSA
The Geostationary Ocean Color Imager (GOCI)  is the first geostationary ocean color satellite sensor which was launched on June 27, 2010. With its six visible bands centered at the wavelengths of 412, 443, 490, 555, 660, and 680 nm and two near-infrared (NIR) bands at wavelengths of 745 and 865 nm, GOCI can monitor the marine environment and provide a variety of ocean optical, biological, and biogeochemical property products for an area of about 2500 × 2500 km2 around the Korean Peninsula. In GOCI measurement, the region is divided into 16 sections for separate slot-shots with a temporal frequency of eight times a day, i.e., from local times of 9:00 to 16:00. GOCI data can be used for various applications such as the short- and long-term regional ocean environment monitoring, disaster and ocean hazard monitoring and prevention, ocean ecosystem and water quality evaluation and analysis, as well as intelligence and national security applications [2, 3].
The western Pacific region (Fig. 1 ) which is covered by GOCI measurement, including the Bohai Sea (BS), Yellow Sea (YS), and East China Sea (ECS), has one of the most turbid waters in the world [4–6]. Total suspended matter (TSM) concentration in the region can reach up to the order of ~100 g m−3 . Significant NIR ocean radiance contributions can be found along the coast of the Yellow Sea and the East China Sea [8, 9]. For example, the normalized water-leaving radiance nLw(λ) [10–13] at the NIR wavelength of 748 nm (nLw(748)) can reach ~3 mW cm−2 μm−1 sr−1 in the Hangzhou Bay of China’s east coastal region . The complexity of the water property in these regions suggests that some existing NIR-modeling schemes [14–16] may not work properly for GOCI ocean color data processing. Thus, satellite ocean color remote sensing in the western Pacific region is significantly limited in these highly turbid coastal regions. It has been shown that the ocean generally is still black for the shortwave infrared (SWIR) bands in the China’s east coastal regions , i.e., ocean water-leaving radiance contributions at the SWIR bands are negligible. Wang et al. (2007; 2011) [9, 17] also show that improved and reasonably accurate nLw(λ) spectra data can be derived using the SWIR-based atmospheric correction algorithm  in the coastal regions of the Yellow Sea and East China Sea , as well as in the highly turbid inland Lake Taihu .
However, because GOCI has no SWIR bands, atmospheric correction for the sensor has been a challenge in order to derive accurate ocean color products in these highly turbid ocean regions. In a recent study, a regional NIR-nLw(λ) model has been proposed for atmospheric correction for ocean color data processing in the western Pacific region, including the Bohai Sea, Yellow Sea, and East China Sea . The new algorithm that was developed based on long-term measurements (2002–2009) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua using the SWIR atmospheric correction algorithm , provides an effective alternative method for GOCI ocean color data processing in these highly turbid ocean regions.
In a collaboration between NOAA Center for Satellite Applications and Research (STAR) and Korea Institute of Ocean Science and Technology (KIOST), we have been working on improving GOCI ocean color products from the standard GOCI data processing , e.g., nLw(λ) spectra data, chlorophyll-a (Chl-a) concentration, diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), etc. In fact, the current standard GOCI data processing  has been significantly improved resulted from our collaboration. In this paper, we report ocean color products derived from GOCI measurements using the new iterative NIR-corrected atmospheric correction algorithm , which is different from the GOCI standard algorithm. Our motivation in this work is to implement the new NIR-corrected atmospheric correction algorithm in the GOCI ocean color data processing for deriving accurate ocean color product data. The accuracy of nLw(λ) spectra data derived from GOCI measurements is assessed and validated with in situ ocean optical and biological measurements, which were collected around Korean Peninsula. We also show example results of the diurnal variation of ocean optical and biogeochemical properties in the region. Furthermore, multi-month composites of ocean color products from GOCI observations are provided and discussed.
2. Data and method
2.1. GOCI ocean color data processing system
The ocean color data processing system at NOAA has been developed based on the NASA satellite ocean color data processing package, i.e., the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS) version 4.6. In addition, the NOAA Multi-Sensor Level-1 to Level-2 (NOAA-MSL12) data processing system has been modified and improved to include: (1) the SWIR and NIR-SWIR ocean color data processing options [18, 21, 22], (2) new aerosol lookup tables including polarization effects  and more accurate Rayleigh radiance computations , (3) the SWIR-based vicarious calibration approach for deriving consistent vicarious gain coefficients for ocean color data processing options of the NIR, SWIR, and NIR-SWIR approaches, (4) incorporated algorithms for detecting absorbing aerosols and turbid waters , (5) implementation of a SWIR-based cloud-masking scheme (particularly over turbid waters) , (6) implementation of an ice-detecting algorithm for global ocean color data processing , and some others, e.g., an approach to improve the performance of MODIS SWIR bands , a method of deriving regional sea ice optical property [29, 30], etc. In the last several years, the NOAA-MSL12 ocean (water) color data processing system has been used to generate improved satellite ocean color product data for global oceans , coastal highly turbid regions , as well as the inland fresh water lakes, e.g., China’s Lake Taihu  and Florida’s Lake Okeechobee .
For the GOCI ocean color data processing, various parameters and lookup tables (LUTs), e.g., Rayleigh radiance LUTs [32, 33], atmospheric diffuse transmittance LUTs [34, 35], spectral solar irradiance data and ozone absorption coefficients , etc., have been generated and modified specifically corresponding to the GOCI eight spectral bands. It is noted that these LUTs and various parameters were generated in the same way as those for MODIS-Aqua using the GOCI spectral response function data. In addition, to accommodate the requirement of the atmospheric correction for GOCI ocean color data processing, a regional NIR-nLw(λ) model has been proposed for atmospheric correction for ocean color data processing in the western Pacific region . Based on the regional empirical relationship between the NIR-nLw(λ) and the diffuse attenuation coefficient Kd(490) , which is derived from long-term MODIS-Aqua measurements (2002–2009) using the SWIR-based ocean color data processing, an iterative scheme with the NIR-based atmospheric correction algorithm  has been developed. Specifically, the following empirical polynomial between the NIR nLw(745) and the diffuse attenuation coefficient Kd(490) has been implemented in the GOCI ocean color data processing:19]. The following nLw(865) versus nLw(745) formula has been implemented in the GOCI ocean color data processing:Eq. (1). Therefore, with this NIR-nLw(λ) model for the western Pacific region, GOCI-NIR nLw(λ) contributions can be estimated and removed in atmospheric correction for the turbid ocean region , e.g., the Bohai Sea, Yellow Sea, and East China Sea.
It is noted that in Eqs. (1) and (2) the same fitting coefficients that were derived from MODIS-Aqua measurements  were used for the GOCI ocean color data processing. The effect of NIR spectral wavelength differences between GOCI and MODIS-Aqua is generally small with small NIR wavelength differences between two sensors, i.e., differed by ~3–4 nm. In fact, the data noise errors in deriving these fitting coefficients  are likely much larger than errors due to spectral band differences between GOCI and MODIS-Aqua. In addition, MODIS-Aqua data from the region have been used to vicariously calibrate GOCI spectral bands (see next section), and in average the effect of the spectral band difference between two sensors has been somewhat accounted for.
2.2. Vicarious calibration for GOCI ocean color products
For the GOCI coverage, the waters in Japan/East Sea are typical of clear Case-1 water. In this study, we have used the MODIS-Aqua-measured nLw(λ) and aerosol property data in the region within the box of 38.2°N–39.2°N and 132.5°E–133.5°E in the central Japan/East Sea as the reference to carry out the sensor on-orbit vicarious calibration [39–41] and derive the vicarious gain coefficients for GOCI eight spectral bands. In effect, GOCI vicarious gains are derived by forcing GOCI-derived nLw(λ) and aerosol models in the region the same as those from MODIS-Aqua.
Specifically, both MODIS-Aqua and GOCI data were remapped to a region centered at 38.7°N and 133°E. MODIS-Aqua aerosol models (Ångström exponents)  and nLw(λ) are derived in the region and used for vicariously calibrating GOCI spectral bands, i.e., GOCI spectral gain coefficients were derived by adjusting the GOCI gains such that the same aerosol Ångström exponent and nLw(λ) values in average are obtained. This is an iterative data process to have final product mean values from GOCI and MODIS-Aqua matched for the calibration scene.
To understand and evaluate the vicarious calibration performance, three data processing methods were used for deriving MODIS-Aqua aerosol and ocean color parameters, i.e., atmospheric correction approaches of the NIR , SWIR , and Wang et al. (2012) NIR-model , respectively. As expected, the GOCI gains derived from the MODIS SWIR and NIR-model approaches are quite consistent, while there are some important differences using the MODIS NIR atmospheric correction method. For GOCI spectral bands of 412, 443, 490, 555, 660, 680, 745, and 865 nm, the MODIS-SWIR derived GOCI gains are: 0.9857, 0.9749, 0.9484, 0.9179, 0.9299, 0.9283, 0.9502, and 1.0, respectively, compared to the method of using Wang et al. (2012) NIR-model-derived GOCI gains of 0.9862, 0.9753, 0.9473, 0.9149, 0.9245, 0.9223, 0.9430, and 1.0, respectively. Results show that the two methods produced consistent GOCI gain coefficients, i.e., in the order of ~0.1%. With the MODIS NIR atmospheric correction method, however, the derived GOCI gains differed by ~1% in comparison with those from the other two approaches. This data analysis gives us confidence to use GOCI vicarious gains derived from the MODIS-Aqua SWIR data processing. Thus, the MODIS-SWIR-derived vicarious calibration gains are applied to the GOCI Level-1B data for ocean color data processing for generating nLw(λ) spectra and other ocean biological and biogeochemical products, e.g., Chl-a concentration, Kd(490) data, etc.
2.3. In situ data
During the period between March and November of 2011, there were extensive field campaigns in ocean regions of the southwest coast of Korea (Yellow Sea), Japan/East Sea, and the East China Sea near Korean Peninsula for the purpose of collecting various in situ physical, optical, and biological ocean data in support of the GOCI calibration and validation, as well as algorithm development efforts [20, 43]. The in situ data collection and processing were carried out following the procedures outlined in the NASA ocean optics protocols .
In particular, in situ hyperspectral ocean water-leaving reflectance spectra data were acquired using the Analytical Spectral Devices, Inc. (ASD) FieldSpec and TriOS RAMSES hyperspectral radiometers . Water-leaving reflectance spectra were derived from the above-surface radiances and sky radiances measured at the nadir angle of 40° and the relative azimuth angle of 90°. In addition, in situ optics data were obtained after applying data quality control procedures described in Moon et al. (2012) .
Water samples that were acquired using Niskin bottles for chlorophyll-a measurements were filtered through 47 mm GF/F filters with nominal pore size of 0.7 μm. From extracted pigments, which were obtained using 90% acetone 10 ml, a dual beam spectrophotometer was used to measure absorbance spectra of the acetone extracted pigment samples. In situ Chl-a data can then be obtained using the measured absorbance spectra following Jeffrey and Humphrey (1975) . Details for in situ data collection, data processing, and data quality can be found in Moon et al. (2012) .
2.4. GOCI-measured ocean color products
Multi-month GOCI Level-1B data from March to December of 2011 were obtained from the Korea Ocean Satellite Center (KOSC). These data were processed into ocean color products with the new atmospheric correction algorithm . We use the in situ data collected during the period of March to November of 2011 to quantify the data quality of GOCI-measured ocean color products and validate the performance of the new atmospheric correction algorithm for GOCI ocean color data processing. Furthermore, we examine the image-wise performance of GOCI products, e.g., ocean features, data spatial continuity and smoothness, data noise, etc., to qualitatively assess GOCI ocean color data product quality. Particularly, diurnal variation of the ocean optical, biological, and biogeochemical properties observed by GOCI are presented and discussed.
3. Results and discussions
3.1. GOCI ocean color products
The GOCI-derived products include nLw(λ) spectra [13, 38], Chl-a concentration [46, 47], and diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)) [37, 48–50]. It should be noted that GOCI Chl-a data were derived using the empirical algorithm [46, 47] and GOCI Kd(490) data were derived using the Wang et al. (2009) algorithm .
Figure 2 provides examples of the GOCI-derived ocean color products in the GOCI coverage region, corresponding to GOCI data acquired at 12:00 (noon) local time on April 5, 2011. Figure 2(a) is the GOCI-measured true color image on April 5, 2011, while Figs. 2(b)–2(f) are nLw(λ) images at GOCI wavelengths of 443, 490, 555, 660, and 865 nm, respectively. GOCI-derived Chl-a and Kd(490) images are shown in Figs. 2(g) and 2(h), respectively. It is noted that there are no retrievals over some extremely turbid regions, e.g., the Hangzhou Bay. This is because of failure in cloud masking, which uses the method from Wang and Shi (2006)  with the NIR spectral reflectance information. The NIR spectral reflectance method  sometimes fails to identify clear sky from extremely turbid waters, e.g., in the Hangzhou Bay in the case of April 5, 2011.
Along the China’s east coastal region, GOCI-measured nLw(λ) values rise with increase of the wavelength from the blue to the green band, and nLw(λ) values for some regions actually peak at the red band. This is a typical characteristic of the sediment-dominated waters, which is consistent with the optical features observed by MODIS-Aqua . Indeed, ocean properties derived from GOCI are comparable to those from MODIS-Aqua observations. It is noted that there are no obvious correlations between GOCI-derived nLw(λ) and aerosol optical thickness (AOT) τa(865)  (results not shown), which implies that the iterative NIR-corrected atmospheric correction approach  performs well in removing the ocean radiance contribution at the NIR bands in order to carry out atmospheric correction properly for GOCI ocean color data processing.
3.2. GOCI ocean color products compared with in situ measurements
To compare the GOCI-derived and the in situ-measured nLw(λ) data, GOCI-measured nLw(λ) data were computed by averaging in 5 × 5 pixels surrounding the in situ measurement location. Specifically, GOCI-measured data at local times of 11:00, 12:00, and 13:00 are used for the comparison analysis. From these three GOCI measurements, the one with the closest measurement time matched with the in situ data was selected and used for the analysis. In fact, the time difference between the GOCI and in situ measurements is within a ~3-hour period, with the minimum and maximum time differences of 5 minutes and about 3 hours, respectively. To have more matchup data points, we used data with relatively large time differences between GOCI and in situ measurements (e.g., > one hour). It should be noted that we received almost all GOCI data at local times of 11:00, 12:00, and 13:00, and in some cases with daily eight images. Figure 1 shows the locations (red dots) of the in situ measurements corresponding to the matchup data set. For the GOCI versus in situ data matchup analyses, we have followed the procedure of Wang et al. (2009) .
Comparison results in Fig. 3 show that the new atmospheric correction algorithm performs reasonably well for the GOCI-derived nLw(λ) data in these highly turbid ocean regions. Indeed, GOCI-derived nLw(λ) spectra at 412, 443, 490, 555, 660, and 680 nm generally match well with the corresponding in situ nLw(λ) measurements (Figs. 3(a)–3(c)). In Fig. 3, the fitting coefficients of slope and intercept (Int.) as well as the correlation coefficient (R) are provided in plots for matchup data of nLw(λ) spectra (linear fitting) (Figs. 3(a)–3(c)) and Chl-a (in log-scale fitting) (Fig. 3(d)). The ratios in nLw(λ) between the GOCI-derived and in situ data show that GOCI retrievals are slightly higher than the in situ measurements, and in particular, for low nLw(λ) values at the blue (412 and 443 nm) bands (i.e., cases with turbid waters) (Fig. 3(a)). Specifically, mean ratios of nLw(λ) at 412, 443, 490, 555, 660, and 680 nm between GOCI and in situ data are 1.166, 1.339, 1.238, 1.133, 1.229, and 1.370, respectively. In fact, the biased high values (high ratios) are mostly due to the cases with low nLw(λ) values. The discrepancies between the GOCI-derived and the in situ-measured nLw(λ) data can be attributed to the time difference between these two measurements (up to ~3 hours) (particularly for turbid waters, e.g., see results in next section), complex ocean environments, and the uncertainty (bias and noise errors) of the empirical optical model to estimate the NIR ocean radiance contributions , e.g., with temporal variation, as well as errors from the in situ measurements . It is particularly noted that most of the in situ measurements were actually located in the coastal regions. Indeed, we found that, with restriction to the time difference within one-hour between GOCI and in situ measurements (but with significantly reduced matchup data amount), mean ratio values of nLw(λ) at 412, 443, 490, 555, 660, and 680 nm can be improved to 1.175, 1.270, 1.178, 1.073, 1.098, and 1.254, respectively. The coastal dynamics such as tidal current, coastal current, and sediment re-suspension may lead to significant changes of the ocean’s optical property within the time difference (up to ~3 hours) between the GOCI and the in situ measurements.
The GOCI-derived two NIR nLw(λ) data, nLw(745) and nLw(865) (Fig. 3(c)), are more or less consistent with the in situ measurements. As an example, the highest nLw(745) from the in situ measurements is ~0.3 mW cm−2 μm−1 sr−1, while the value derived from the GOCI is ~0.34–0.35 mW cm−2 μm−1 sr−1. This shows that nLw(λ) values at the NIR bands can be effectively estimated and removed from the GOCI-measured top-of-atmosphere (TOA) radiances using the Wang et al. (2012) model . Thus, reasonably accurate atmospheric correction can be carried out to derive nLw(λ) spectra data from GOCI measurements.
With GOCI-measured nLw(λ) spectra data, other ocean biological and biogeochemical products such as Chl-a concentration and Kd(490) data can be derived. Figure 3(d) shows the comparison between GOCI-measured Chl-a data and the corresponding in situ measurements. Although Fig. 3(d) shows some overall consistence in GOCI-derived Chl-a data compared with in situ measurements, the mean ratio (GOCI versus in situ) of Chl-a in Fig. 3(d) is 1.435. This shows some overestimations of GOCI-measured Chl-a values. The GOCI-derived Chl-a uncertainty is primarily due to the limitation of the empirical Chl-a algorithm [46, 47], which is mostly applicable to clear open oceans and may not be valid over such highly turbid waters. Overall, however, these comparison results show that GOCI-derived ocean color products can be used to quantify and characterize ocean optical, biological, and biogeochemical properties and study their related ocean processes in the western Pacific region.
3.3. Diurnal variation of ocean property in the Bohai Sea
With possibly eight-time measurements daily, GOCI provides a unique capability to monitor the ocean environments in near real-time, and GOCI data can be used to address the diurnal variability in the ecosystem of the entire GOCI coverage region. Here, a case study of ocean diurnal changes from GOCI measurements is provided and discussed. The GOCI results demonstrate that GOCI can provide real-time monitoring of water optical, biological, and biogeochemical variability of the ocean ecosystem in the western Pacific region.
An example from GOCI measurements. Figure 4 shows an example of the diurnal change in Kd(490) in the Bohai Sea region on April 5, 2011. In general, the Bohai Sea is dominated with turbid waters with Kd(490) over ~1.0 m−1 for most part of the region between local times 9:00 and 16:00. Even though variations in Kd(490) between two neighboring GOCI observations (one-hour apart) are not obviously notable, the progressive change in terms of Kd(490) patterns and magnitudes from local time 9:00 to 16:00 is clearly shown in Figs. 4(a)–4(h). At the local time of 9:00 on April 5, 2011, Kd(490) ranged between ~1.4–1.5 m−1 for most part of the Bohai Sea (Fig. 4(a)). The coverage of waters with Kd(490) between ~1.4–1.5 m−1 gradually decreased at local times of 10:00 (Fig. 4(b)), 11:00 (Fig. 4(c)), 12:00 (Fig. 4(d)), and 13:00 (Fig. 4(e)). At the local time of 13:00, coverage of waters with Kd(490) values of ~1.4–1.5 m−1 is less than half of the Bohai Sea. The change of the ocean environments in terms of Kd(490) variation in a 4-hour period is quite notable. It is noted that the fingerlike features with south-north orientation actually mark the sea surface signatures of underwater sand ridges in the Bohai Sea .
At the local time 14:00, Kd(490) value in the region was slightly higher than that observed one hour earlier (Fig. 4(f)). Different from the Kd(490) trend between local times 9:00 and 13:00, Kd(490) in the Bohai Sea showed slightly increase after the local time 14:00 (Fig. 4(g)). This is reflected with slightly enhanced Kd(490) in the sand ridge regions near Liaodong Peninsula . At a local time 16:00, most part of the Bohai Sea was covered with clouds (Fig. 4(h)). However, Kd(490) values still showed noticeable increase for the portion with Kd(490) retrievals in the northeastern Bohai Sea, suggesting that Kd(490) between local 15:00 and 16:00 also increased for a broad Bohai Sea region.
Some quantitative evaluations. Using a region of 50 × 50 km2 centered at a location of [38.89°N, 120.17°E] in the central Bohai Sea as a marked box in Fig. 4(a), we further quantify the diurnal variability in nLw(λ) and Kd(490) on April 5, 2011 (Fig. 5 ). For the central Bohai Sea region on April 5, 2011, nLw(λ) values at wavelengths of 412, 443, and 490 nm increased from local times of 9:00 to 12:00 (noon), peaked at the local noon, and then reduced in afternoon. However, from the local time of 14:00 to 15:00, nLw(443) and nLw(490) slightly increased. On the other hand, nLw(λ) values at wavelengths of 555, 660, and 680 nm decreased from the local morning (9:00) to the local afternoon at 13:00, and then slightly increased from local time of 13:00 to 15:00. Ocean biological changes can be inferred from nLw(λ) spectra variations. The changes in nLw(λ) at green and red bands in the region are consistent with the Kd(490) variations. Indeed, Kd(490) in the central Bohai Sea showed significant changes between local times of 9:00 and 15:00. From a period of 9:00 to 13:00, Kd(490) in the central Bohai Sea trended lower from ~1.5 m−1 to ~1.05 m−1. After local time 13:00, Kd(490) inched higher to ~1.15 m−1. Diurnal variations of other ocean properties such as TSM concentration  in the Bohai Sea are also assessed and show similar trends (results not shown).
Discussions. Results in Figs. 4 and 5 show that the diurnal variability of ocean environments can be effectively monitored with the geostationary ocean color satellite as suggested by Neukermans et al. (2012) . Diurnal variability in nLw(λ) is also remarkable as shown in Fig. 5(a). GOCI-measured nLw(λ) values at the blue bands have highs at local noon, while nLw(λ) values at green and red bands have lows at early afternoon (at local time 13:00). Different spectral shapes in nLw(λ) suggest that diurnal variability as revealed by GOCI in the Bohai Sea is not the artifact caused by the inaccurate atmospheric correction due to different geometries for the 8-GOCI measurements (as also shown from nLw(λ) validation results in the previous section). In addition, the optical spectra for all the 7 observations as shown in Fig. 5 are all reasonable and similar to the typical optical spectra of the turbid waters in the Bohai Sea . Further examination of AOT data (results not shown here) for the seven GOCI measurements shows that the diurnal changes of AOT is relatively small in the Bohai Sea region between local times of 9:00 and 15:00. There is no regional-wide AOT increase or decrease between different measurements. All these provide evidences that atmospheric correction for GOCI ocean color data processing is robust for different GOCI observations on the same day, and the variability of Kd(490) and nLw(λ) reflects the real diurnal changes of ocean environment in the Bohai Sea.
For the ocean environment, diurnal variability can generally be caused by a variety of ocean’s physical and biological processes such as tides, biological cycles, diurnal winds, etc. For the case of April 5, 2011 (Figs. 4 and 5), the wind speed was quite low and stable. The mean wind speeds in the study region in Fig. 4 varied from ~3.5 to ~3.7 m/s during the GOCI eight measurements on that day. Because the results of notable diurnal variability (Fig. 5) are from the region with water depth over 30 m, wind-driven wave is not the driving force that could re-suspend the sediment from the ocean bottom and consequently was observed by GOCI. It requires much shallow water and large winds to show the effect of the wind-driven wave on the sediment re-suspension from the ocean bottom.
Even though the diurnal biological variability can occur (such as showing in Chl-a and inherent optical property (IOP) variations) [53, 54], the ocean biological diurnal variation is generally small and the maximum of Chl-a (as well as Kd(490)) is normally observed in the early afternoon as the result of phytoplankton growth, grazing, and physiological responses . In the GOCI observations of this study, Kd(490) actually reached minimum in the early afternoon (Fig. 5). This indicates that the diurnal variability as shown in Figs. 4 and 5 is not primarily from a result of the ocean diurnal biological change.
In the Bohai Sea, Yellow Sea, and East China Sea, however, a semidiurnal and diurnal tide plays a significant role on the dynamics of the ocean environments [56–58]. In fact, variations of the satellite ocean color observations within a spring-neap tidal cycle are in the same order as the seasonal change as observed by MODIS-Aqua in this region . Some studies have shown that TSM in the water column is strongly correlated to tidal currents in a semidiurnal/diurnal cycle [60, 61]. Thus, of all the possible ocean processes that can drive the diurnal change of ocean environments in the region, diurnal variability as observed in this study is mainly driven by the change of the tidal currents in the Bohai Sea. The tidal dynamics (tidal current) is the most important factor influencing the variation of the TSM (thus Kd(490)) in the region.
3.4. GOCI-measured composite ocean color products
Using GOCI measurements at local 12:00 (noon) from March to December of 2011 (10 months data), GOCI composite ocean color images are generated for nLw(412) (Fig. 6(a) ), nLw(443) (Fig. 6(b)), nLw(490) (Fig. 6(c)), nLw(555) (Fig. 6(d)), nLw(660) (Fig. 6(e)), nLw(680) (Fig. 6(f)), nLw(865) (Fig. 6(g)), Chl-a (Fig. 6(h)), and Kd(490) (Fig. 6(i)). The enhancements of nLw(λ) at blue (443 nm), green (555 nm), and red (660 nm) are consistent with the elevated Kd(490) in coastal regions. In the Japan/East Sea, Kd(490) values are lower than those in the Bohai Sea, Yellow Sea, and East China Sea. This is also reflected with a flat nLw(660) (red band) distribution and near zero nLw(865) in the NIR wavelength. In the southeastern portion of GOCI coverage region in east of the East China Sea and South China Sea, waters are featured with low Chl-a, low Kd(490), and high nLw(443) (blue band). The boundary and ocean front between the open ocean oligotrophic and coastal waters can be clearly identified in the nLw(412) and nLw(443) (Figs. 6(a) and 6(b)), Chl-a (Fig. 6(h)), and Kd(490) (Fig. 6(i)), and match the classic path of the Kuroshio Current.
Significantly enhanced nLw(555) and nLw(660) are located in the Yellow River estuary in the Bohai Sea, the Subei Shoal in the Yellow Sea, and the Yangtze River estuary and the Hangzhou Bay in the East China Sea. Values of nLw(555) and nLw(660) are over ~5 mW cm−2 μm−1 sr−1 for these highly turbid ocean regions. For the modestly turbid regions, such as plumes in the central East China Sea , nLw(λ) is more enhanced in the green band than that in the red band. Compared to the climatology of the ocean color products derived with 8-year observations from MODIS-Aqua , GOCI-measured nLw(λ) data in this study are quantitatively consistent with the climatology of the nLw(λ) in the region. This provides further evidence that GOCI-derived ocean color products from the proposed atmospheric correction approach are reasonably accurate and can be used to quantify and characterize both short- and long-term variability of the ocean ecosystem in the highly turbid ocean regions.
It should be noted that, since the complete image for GOCI coverage is generated with 16 slot-shots as a mosaic, the boundary effect of different GOCI observation slots is shown in the composite image maps in Fig. 6, as well as in other GOCI Level-2 images (e.g., Fig. 2). The artificial boundary caused by GOCI’s 2D frame image capture mode is still an ongoing issue, and the issue is being resolved in an effort at KIOST.
We have derived the GOCI normalized water-leaving radiance spectra nLw(λ) for the GOCI coverage region using an iterative NIR-corrected atmospheric correction algorithm . GOCI-derived ocean color products are compared with the in situ measurements. The validation results from this effort show a reasonably good agreement between GOCI-derived values and in situ measurements. Multi-month composites of GOCI ocean color products in this region are also quantitatively consistent with corresponding composites of MODIS-Aqua ocean color products that were derived using the SWIR-based atmospheric correction algorithm. This demonstrates that, using the new atmospheric correction algorithm in processing GOCI data, ocean color products can be used to characterize and quantify the ocean environments in the western Pacific region.
This study also shows that GOCI observations can be used to characterize and quantify the diurnal variability of the marine ecosystem for the western Pacific region. Our results show that there are significant diurnal variations in ocean optical, biological, and biogeochemical properties in the region. In particular, as an example for the Bohai Sea in April, the diurnal variability in nLw(λ) has different spectral variation. GOCI-measured nLw(λ) values at the blue bands have highs at local noon, while nLw(λ) values at green and red bands have lows at early afternoon (13:00 at local time). Such large scale (spatial) and high temporal resolution measurements can only be achieved by geostationary satellite sensor [62, 63]. Furthermore, this unique capability from geostationary satellite sensor can complement the ocean color observations from other polar-orbiting satellite sensors such as MODIS and the Visible Infrared Imaging Radiometer Suite (VIIRS), which have a global coverage, but lack the temporal resolution to monitor the dynamics of marine environments on an hourly basis.
The GOCI Level-1B data and in situ data used in this study were provided by Korea Institute of Ocean Science and Technology (KIOST). We thank two anonymous reviewers for their useful comments. The views, opinions, and findings contained in this paper are those of the authors and should not be construed as an official NOAA or U.S. Government position, policy, or decision.
References and links
1. S. Cho, Y. H. Ahn, J. H. Ryu, G. Kang, and H. Youn, “Development of Geostationary Ocean Color Imager (GOCI),” Korean J. Remote Sens. 26, 157–165 (2010).
2. J. K. Choi, Y. J. Park, J. H. Ahn, H. S. Lim, J. Eom, and J. H. Ryu, “GOCI, the world's first geostationary ocean color observation satellite, for the monitoring of temporal variability in coastal water turbidity,” J. Geophys. Res. 117(C9), C09004 (2012), doi:. [CrossRef]
3. J. H. Ryu, J. K. Choi, J. Eom, and J. H. Ahn, “Temporal variation in Korean coastal waters using Geostationary Ocean Color Imager,” J. Coast. Res. 64, 1731–1735 (2011).
4. W. Shi and M. Wang, “Characterization of global ocean turbidity from Moderate Resolution Imaging Spectroradiometer ocean color observations,” J. Geophys. Res. 115(C11), C11022 (2010), doi:. [CrossRef]
5. W. Shi and M. Wang, “Satellite observations of the seasonal sediment plume in central East China Sea,” J. Mar. Syst. 82(4), 280–285 (2010). [CrossRef]
6. W. Shi and M. Wang, “Satellite views of the Bohai Sea, Yellow Sea, and East China Sea,” Prog. Oceanogr. 104, 30–45 (2012). [CrossRef]
7. M. Zhang, J. Tang, Q. Dong, Q. Song, and J. Ding, “Retrieval of total suspended matter concentration in the Yellow and East China Seas from MODIS imagery,” Remote Sens. Environ. 114(2), 392–403 (2010). [CrossRef]
8. W. Shi and M. Wang, “An assessment of the black ocean pixel assumption for MODIS SWIR bands,” Remote Sens. Environ. 113(8), 1587–1597 (2009). [CrossRef]
9. M. Wang, J. Tang, and W. Shi, “MODIS-derived ocean color products along the China east coastal region,” Geophys. Res. Lett. 34(6), L06611 (2007), doi:. [CrossRef]
10. A. Morel and B. Gentili, “Diffuse reflectance of oceanic waters: its dependence on Sun angle as influenced by the molecular scattering contribution,” Appl. Opt. 30(30), 4427–4438 (1991). [CrossRef]
13. IOCCG, Atmospheric correction for remotely-sensed ocean-colour products, M. Wang (Ed.), Reports of International Ocean-Color Coordinating Group, No. 10, IOCCG, Dartmouth, Canada (2010).
14. R. P. Stumpf, R. A. Arnone, R. W. Gould, P. M. Martinolich, and V. Ransibrahmanakul, “A partially coupled ocean-atmosphere model for retrieval of water-leaving radiance from SeaWiFS in coastal waters,” (NASA Goddard Space Flight Center, Greenbelt, Maryland, 2003), pp. 51–59.
15. S. W. Bailey, B. A. Franz, and P. J. Werdell, “Estimation of near-infrared water-leaving reflectance for satellite ocean color data processing,” Opt. Express 18(7), 7521–7527 (2010). [CrossRef]
17. M. Wang, W. Shi, and J. Tang, “Water property monitoring and assessment for China's inland Lake Taihu from MODIS-Aqua measurements,” Remote Sens. Environ. 115(3), 841–854 (2011). [CrossRef]
19. M. Wang, W. Shi, and L. Jiang, “Atmospheric correction using near-infrared bands for satellite ocean color data processing in the turbid western Pacific region,” Opt. Express 20(2), 741–753 (2012). [CrossRef]
20. J. H. Ahn, Y. J. Park, J. H. Ryu, B. Lee, and I. S. Oh, “Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI),” Ocean Sci. J. 47(3), 247–259 (2012). [CrossRef]
22. M. Wang, S. Son, and W. Shi, “Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithm using SeaBASS data,” Remote Sens. Environ. 113(3), 635–644 (2009). [CrossRef]
24. M. Wang, “A refinement for the Rayleigh radiance computation with variation of the atmospheric pressure,” Int. J. Remote Sens. 26(24), 5651–5663 (2005). [CrossRef]
25. W. Shi and M. Wang, “Detection of turbid waters and absorbing aerosols for the MODIS ocean color data processing,” Remote Sens. Environ. 110(2), 149–161 (2007). [CrossRef]
26. M. Wang and W. Shi, “Cloud masking for ocean color data processing in the coastal regions,” IEEE Trans. Geosci. Rem. Sens. 44(11), 3196–3205 (2006). [CrossRef]
27. M. Wang and W. Shi, “Detection of ice and mixed ice-water pixels for MODIS ocean color data processing,” IEEE Trans. Geosci. Rem. Sens. 47(8), 2510–2518 (2009). [CrossRef]
28. M. Wang and W. Shi, “Sensor noise effects of the SWIR bands on MODIS-derived ocean color products,” IEEE Trans. Geosci. Rem. Sens. 50(9), 3280–3292 (2012). [CrossRef]
29. W. Shi and M. Wang, “Sea ice properties in the Bohai Sea measured by MODIS-Aqua: 1. Satellite algorithm development,” J. Mar. Syst. 95, 32–40 (2012). [CrossRef]
30. W. Shi and M. Wang, “Sea ice properties in the Bohai Sea measured by MODIS-Aqua: 2. Study of sea ice seasonal and interannual variability,” J. Mar. Syst. 95, 41–49 (2012). [CrossRef]
31. M. Wang, C. J. Nim, S. Son, and W. Shi, “Characterization of turbidity in Florida’s Lake Okeechobee and Caloosahatchee and St. Lucie estuaries using MODIS-Aqua measurements,” Water Res. 46(16), 5410–5422 (2012). [CrossRef]
33. M. Wang, “The Rayleigh lookup tables for the SeaWiFS data processing: Accounting for the effects of ocean surface roughness,” Int. J. Remote Sens. 23(13), 2693–2702 (2002). [CrossRef]
35. H. Yang and H. R. Gordon, “Remote sensing of ocean color: assessment of water-leaving radiance bidirectional effects on atmospheric diffuse transmittance,” Appl. Opt. 36(30), 7887–7897 (1997). [CrossRef]
36. M. Wang, “A sensitivity study of SeaWiFS atmospheric correction algorithm: Effects of spectral band variations,” Remote Sens. Environ. 67(3), 348–359 (1999). [CrossRef]
37. M. Wang, S. Son, and J. L. W. Harding Jr., “Retrieval of diffuse attenuation coefficient in the Chesapeake Bay and turbid ocean regions for satellite ocean color applications,” J. Geophys. Res. 114(C10), C10011 (2009), doi:. [CrossRef]
38. H. R. Gordon and M. Wang, “Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm,” Appl. Opt. 33(3), 443–452 (1994). [CrossRef]
39. H. R. Gordon, “In-orbit calibration strategy for ocean color sensors,” Remote Sens. Environ. 63(3), 265–278 (1998). [CrossRef]
40. M. Wang and H. R. Gordon, “Calibration of ocean color scanners: How much error is acceptable in the near-infrared,” Remote Sens. Environ. 82(2-3), 497–504 (2002). [CrossRef]
41. B. A. Franz, S. W. Bailey, P. J. Werdell, and C. R. McClain, “Sensor-independent approach to the vicarious calibration of satellite ocean color radiometry,” Appl. Opt. 46(22), 5068–5082 (2007). [CrossRef]
42. M. Wang, K. D. Knobelspiesse, and C. R. McClain, “Study of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) aerosol optical property data over ocean in combination with the ocean color products,” J. Geophys. Res. 110(D10), D10S06 (2005), doi:. [CrossRef]
43. J. E. Moon, Y. J. Park, J. H. Ryu, J. K. Choi, J. H. Ahn, J. E. Min, Y. B. Son, S. J. Lee, H. J. Han, and Y. H. Ahn, “Initial validation of GOCI water products against in situ data collected around Korean Peninsula for 2010–2011,” Ocean Sci. J. 47(3), 261–277 (2012). [CrossRef]
44. J. M. Mueller and G. S. Fargion, “Ocean optics protocols for satellite ocean color sensor validation, Revision 3, Part I & II,” (NASA Goddard Space Flight Center, Greenbelt, Maryland, 2002), pp. 1–308.
45. S. W. Jeffrey and G. F. Humphrey, “New spectrophotometric equation for determining chlorophyll a, b, c1 and c2,” Biochem. Physiol. Pflanz. 167, 194–204 (1975).
46. J. E. O'Reilly, S. Maritorena, B. G. Mitchell, D. A. Siegel, K. L. Carder, S. A. Garver, M. Kahru, and C. R. McClain, “Ocean color chlorophyll algorithms for SeaWiFS,” J. Geophys. Res. 103(C11), 24937–24953 (1998). [CrossRef]
47. J. E. O'Reilly, S. Maritorena, D. A. Siegel, M. C. O'Brien, D. Toole, B. G. Mitchell, M. Kahru, F. P. Chavez, P. Strutton, G. F. Cota, S. B. Hooker, C. R. McClain, K. L. Carder, F. Muller-Karger, L. Harding, A. Magnuson, D. Phinney, G. F. Moore, J. Aiken, K. R. Arrigo, R. Letelier, and M. Culver, “Ocean color chlorophyll a algorithms for SeaWiFS, OC2 and OC4: Version 4,” (S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 2000), pp. 8–22.
48. J. L. Mueller, “SeaWiFS algorithm for the diffuse attenuation coefficient, K(490), using water-leaving radiances at 490 and 555 nm,” (NASA Goddard Space Flight Center, Greenbelt, Maryland, 2000), pp. 24–27.
49. A. Morel, Y. Huot, B. Gentili, P. J. Werdell, S. B. Hooker, and B. A. Franz, “Examining the consistency of products derived from various ocean color sensors in open ocean (Case 1) waters in the perspective of a multi-sensor approach,” Remote Sens. Environ. 111(1), 69–88 (2007). [CrossRef]
50. Z. P. Lee, M. Darecki, K. Carder, C. Davis, D. Stramski, and W. Rhea, “Diffuse attenuation coefficient of downwelling irradiance: An evaluation of remote sensing methods,” J. Geophys. Res. 110(C2), C02017 (2005), doi:. [CrossRef]
51. W. Shi, M. Wang, X. Li, and W. G. Pichel, “Ocean sand ridge signatures in the Bohai Sea observed by satellite ocean color and synthetic aperture radar measurements,” Remote Sens. Environ. 115(8), 1926–1934 (2011). [CrossRef]
52. G. Neukermans, K. G. Ruddick, and N. Greenwood, “Diurnal variability of turbidity and light attenuation in the southern North Sea from SEVIRI geostationary sensor,” Remote Sens. Environ. 124, 564–580 (2012). [CrossRef]
53. G. Dall'Olmo, E. Boss, M. J. Behrenfeld, T. K. Westberry, C. Courties, L. Prieur, M. Pujo-Pay, N. Hardman-Mountford, and T. Moutin, “Inferring phytoplankton carbon and eco-physiological rates from diel cycles of spectral particulate beam-attenuation coefficient,” Biogeosciences 8(11), 3423–3439 (2011). [CrossRef]
54. H. Loisel, V. Vantrepotte, K. Norkvist, X. Meriaux, M. Kheireddine, J. Ras, M. Pujo-Pay, Y. Combet, K. Leblanc, G. Dall'Olmo, R. Mauriac, D. Dessailly, and T. Moutin, “Characterization of the bio-optical anomaly and diurnal variability of particulate matter, as seen from scattering and backscattering coefficients, in ultra-oligotrophic eddies of the Mediterranean Sea,” Biogeosciences 8(11), 3295–3317 (2011). [CrossRef]
55. J. Neveux, C. Dupouy, J. Blanchot, A. L. Bouteiller, M. R. Landry, and S. L. Brown, “Diel dynamics of chlorophylls in high-nutrient, low-chlorophyll waters of the equatorial Pacific (180 degrees): Interactions of growth, grazing, physiological responses, and mixing,” J. Geophys. Res. 108(C12), 8140 (2003), . [CrossRef]
56. X. Guo and T. Yanagi, “Three-dimensional structure of tidal current in the East China Sea and Yellow Sea,” J. Oceanogr. 54(6), 651–668 (1998). [CrossRef]
57. T. Yanagi and K. Inoue, “Tide and tidal current in the Yellow/East China Seas,” Mer (Paris) 32, 153–165 (1994).
58. T. Yanagi, A. Morimoto, and K. Ichikawa, “Co-tidal and co-range charts for the East China Sea and the Yellow Sea derived from satellite altimetric data,” J. Oceanogr. 53, 303–310 (1997).
59. W. Shi, M. Wang, and L. Jiang, “Spring-neap tidal effects on satellite ocean color observations in the Bohai Sea, Yellow Sea, and East China Sea,” J. Geophys. Res. 116(C12), C12032 (2011), doi:. [CrossRef]
60. R. J. Uncles, J. A. Stephens, and R. E. Smith, “The dependence of estuarine turbidity on tidal intrusion length, tidal range and residence time,” Cont. Shelf Res. 22(11-13), 1835–1856 (2002). [CrossRef]
61. S. L. Yang, J. Zhang, and J. Zhu, “Response of suspended sediment concentration to tidal dynamics at a site inside the mouth of an inlet: Jiaozhou Bay (China),” Hydrol. Earth Syst. Sci. 8(2), 170–182 (2004). [CrossRef]
62. IOCCG, Ocean-colour observations from a geostationary orbit, D. Antoine (Ed.), Reports of International Ocean-Color Coordinating Group, No. 12, IOCCG, Dartmouth, Canada (2012).
63. J. Fishman, L. T. Iraci, J. Al-Saadi, K. Chance, F. Chavez, M. Chin, P. Coble, C. Davis, P. M. DiGiacomo, D. Edwards, A. Eldering, J. Goes, J. Herman, C. Hu, D. J. Jacob, C. Jordan, S. R. Kawa, R. Key, X. Liu, S. Lohrenz, A. Mannino, V. Natraj, D. Neil, J. Neu, M. Newchurch, K. Pickering, J. Salisbury, H. Sosik, A. Subramaniam, M. Tzortziou, J. Wang, and M. Wang, “The United States' next generation of atmospheric composition and coastal ecosystem measurements: NASA's geostationary coastal and air pollution events (GEO-CAPE) mission,” Bull. Am. Meteorol. Soc. 93(10), 1547–1566 (2012). [CrossRef]